Overview

Dataset statistics

Number of variables11
Number of observations235192
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.7 MiB
Average record size in memory88.0 B

Variable types

DateTime1
Text1
Categorical1
Numeric8

Alerts

Series has constant value ""Constant
Close is highly overall correlated with High and 3 other fieldsHigh correlation
High is highly overall correlated with Close and 3 other fieldsHigh correlation
Low is highly overall correlated with Close and 3 other fieldsHigh correlation
Open is highly overall correlated with Close and 3 other fieldsHigh correlation
Prev Close is highly overall correlated with Close and 3 other fieldsHigh correlation

Reproduction

Analysis started2024-05-24 17:46:44.437945
Analysis finished2024-05-24 17:47:08.924555
Duration24.49 seconds
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

Date
Date

Distinct5306
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Minimum2000-01-02 00:00:00
Maximum2021-12-04 00:00:00
2024-05-24T23:17:09.029172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:17:09.199324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Symbol
Text

Distinct65
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2024-05-24T23:17:09.379128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length6.8946648
Min length2

Characters and Unicode

Total characters1621570
Distinct characters28
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowADANIPORTS
2nd rowADANIPORTS
3rd rowADANIPORTS
4th rowADANIPORTS
5th rowADANIPORTS
ValueCountFrequency (%)
itc 5306
 
2.3%
m&m 5306
 
2.3%
wipro 5306
 
2.3%
asianpaint 5306
 
2.3%
sbin 5306
 
2.3%
reliance 5306
 
2.3%
ongc 5306
 
2.3%
icicibank 5306
 
2.3%
bpcl 5306
 
2.3%
sunpharma 5306
 
2.3%
Other values (55) 182132
77.4%
2024-05-24T23:17:09.724517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 178342
 
11.0%
I 159662
 
9.8%
N 129461
 
8.0%
T 127424
 
7.9%
C 112348
 
6.9%
E 110801
 
6.8%
R 90435
 
5.6%
O 84053
 
5.2%
S 80449
 
5.0%
L 72106
 
4.4%
Other values (18) 476489
29.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1611143
99.4%
Other Punctuation 5306
 
0.3%
Dash Punctuation 3202
 
0.2%
Decimal Number 1919
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 178342
 
11.1%
I 159662
 
9.9%
N 129461
 
8.0%
T 127424
 
7.9%
C 112348
 
7.0%
E 110801
 
6.9%
R 90435
 
5.6%
O 84053
 
5.2%
S 80449
 
5.0%
L 72106
 
4.5%
Other values (15) 466062
28.9%
Other Punctuation
ValueCountFrequency (%)
& 5306
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3202
100.0%
Decimal Number
ValueCountFrequency (%)
0 1919
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1611143
99.4%
Common 10427
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 178342
 
11.1%
I 159662
 
9.9%
N 129461
 
8.0%
T 127424
 
7.9%
C 112348
 
7.0%
E 110801
 
6.9%
R 90435
 
5.6%
O 84053
 
5.2%
S 80449
 
5.0%
L 72106
 
4.5%
Other values (15) 466062
28.9%
Common
ValueCountFrequency (%)
& 5306
50.9%
- 3202
30.7%
0 1919
 
18.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1621570
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 178342
 
11.0%
I 159662
 
9.8%
N 129461
 
8.0%
T 127424
 
7.9%
C 112348
 
6.9%
E 110801
 
6.8%
R 90435
 
5.6%
O 84053
 
5.2%
S 80449
 
5.0%
L 72106
 
4.4%
Other values (18) 476489
29.4%

Series
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
EQ
235192 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters470384
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEQ
2nd rowEQ
3rd rowEQ
4th rowEQ
5th rowEQ

Common Values

ValueCountFrequency (%)
EQ 235192
100.0%

Length

2024-05-24T23:17:09.882352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-24T23:17:10.001012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
eq 235192
100.0%

Most occurring characters

ValueCountFrequency (%)
E 235192
50.0%
Q 235192
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 470384
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 235192
50.0%
Q 235192
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 470384
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 235192
50.0%
Q 235192
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 470384
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 235192
50.0%
Q 235192
50.0%

Prev Close
Real number (ℝ)

HIGH CORRELATION 

Distinct63729
Distinct (%)27.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1266.1963
Minimum0
Maximum32861.95
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-05-24T23:17:10.183848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile97.1275
Q1274.3
median566.5
Q31242.2
95-th percentile3976.3675
Maximum32861.95
Range32861.95
Interquartile range (IQR)967.9

Descriptive statistics

Standard deviation2581.3703
Coefficient of variation (CV)2.0386809
Kurtosis48.856414
Mean1266.1963
Median Absolute Deviation (MAD)368
Skewness6.2772378
Sum2.9779925 × 108
Variance6663472.7
MonotonicityNot monotonic
2024-05-24T23:17:10.615681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
137 32
 
< 0.1%
140 30
 
< 0.1%
30 30
 
< 0.1%
200.15 30
 
< 0.1%
157.1 29
 
< 0.1%
270.05 29
 
< 0.1%
135 29
 
< 0.1%
125.2 28
 
< 0.1%
171.95 28
 
< 0.1%
34 28
 
< 0.1%
Other values (63719) 234899
99.9%
ValueCountFrequency (%)
0 1
 
< 0.1%
2 1
 
< 0.1%
9.15 1
 
< 0.1%
9.4 1
 
< 0.1%
9.5 1
 
< 0.1%
9.6 1
 
< 0.1%
9.65 6
< 0.1%
9.75 3
< 0.1%
9.8 2
 
< 0.1%
9.85 2
 
< 0.1%
ValueCountFrequency (%)
32861.95 1
< 0.1%
32766.1 1
< 0.1%
32661.85 1
< 0.1%
32617.75 1
< 0.1%
32498.6 1
< 0.1%
32443.5 1
< 0.1%
32403.95 1
< 0.1%
32371.05 1
< 0.1%
32233.4 1
< 0.1%
32226.4 1
< 0.1%

Open
Real number (ℝ)

HIGH CORRELATION 

Distinct44298
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1267.7597
Minimum8.5
Maximum33399.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-05-24T23:17:10.805265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.5
5-th percentile97.3
Q1275
median567.025
Q31243.3125
95-th percentile3980.835
Maximum33399.95
Range33391.45
Interquartile range (IQR)968.3125

Descriptive statistics

Standard deviation2585.2596
Coefficient of variation (CV)2.0392347
Kurtosis48.908252
Mean1267.7597
Median Absolute Deviation (MAD)368.075
Skewness6.2802546
Sum2.9816694 × 108
Variance6683567.2
MonotonicityNot monotonic
2024-05-24T23:17:11.044944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
370 210
 
0.1%
340 208
 
0.1%
330 203
 
0.1%
350 197
 
0.1%
360 194
 
0.1%
315 192
 
0.1%
290 191
 
0.1%
345 189
 
0.1%
260 187
 
0.1%
320 184
 
0.1%
Other values (44288) 233237
99.2%
ValueCountFrequency (%)
8.5 1
 
< 0.1%
9.5 1
 
< 0.1%
9.6 1
 
< 0.1%
9.65 1
 
< 0.1%
9.75 4
< 0.1%
9.8 2
< 0.1%
9.85 4
< 0.1%
9.9 2
< 0.1%
9.95 2
< 0.1%
10 3
< 0.1%
ValueCountFrequency (%)
33399.95 1
< 0.1%
32800 1
< 0.1%
32698 1
< 0.1%
32599 1
< 0.1%
32500 2
< 0.1%
32499 1
< 0.1%
32489 1
< 0.1%
32480 1
< 0.1%
32451.5 1
< 0.1%
32301.2 1
< 0.1%

High
Real number (ℝ)

HIGH CORRELATION 

Distinct49036
Distinct (%)20.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1286.5814
Minimum9.75
Maximum33480
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-05-24T23:17:11.255634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.75
5-th percentile99.35
Q1279.5
median576.9
Q31263
95-th percentile4030
Maximum33480
Range33470.25
Interquartile range (IQR)983.5

Descriptive statistics

Standard deviation2619.6492
Coefficient of variation (CV)2.0361317
Kurtosis48.700878
Mean1286.5814
Median Absolute Deviation (MAD)374
Skewness6.270576
Sum3.0259366 × 108
Variance6862562
MonotonicityNot monotonic
2024-05-24T23:17:11.433225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
360 128
 
0.1%
375 125
 
0.1%
370 123
 
0.1%
330 122
 
0.1%
350 121
 
0.1%
275 120
 
0.1%
315 120
 
0.1%
355 119
 
0.1%
280 118
 
0.1%
305 115
 
< 0.1%
Other values (49026) 233981
99.5%
ValueCountFrequency (%)
9.75 1
 
< 0.1%
9.8 4
< 0.1%
9.85 2
< 0.1%
9.9 3
< 0.1%
9.95 1
 
< 0.1%
10 4
< 0.1%
10.1 2
< 0.1%
10.15 2
< 0.1%
10.2 1
 
< 0.1%
10.25 1
 
< 0.1%
ValueCountFrequency (%)
33480 1
< 0.1%
33328.8 1
< 0.1%
32916.85 1
< 0.1%
32799.2 1
< 0.1%
32766.6 1
< 0.1%
32750 1
< 0.1%
32698.95 1
< 0.1%
32644.95 1
< 0.1%
32604.25 1
< 0.1%
32549 1
< 0.1%

Low
Real number (ℝ)

HIGH CORRELATION 

Distinct51335
Distinct (%)21.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1247.4885
Minimum8.5
Maximum32468.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-05-24T23:17:11.609390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.5
5-th percentile95.2
Q1269.6
median556.5
Q31221.65
95-th percentile3926.8225
Maximum32468.1
Range32459.6
Interquartile range (IQR)952.05

Descriptive statistics

Standard deviation2546.6214
Coefficient of variation (CV)2.0413988
Kurtosis49.01702
Mean1247.4885
Median Absolute Deviation (MAD)362
Skewness6.284039
Sum2.9339931 × 108
Variance6485280.5
MonotonicityNot monotonic
2024-05-24T23:17:11.783967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300 134
 
0.1%
325 130
 
0.1%
370 126
 
0.1%
320 123
 
0.1%
390 120
 
0.1%
310 120
 
0.1%
350 119
 
0.1%
260 112
 
< 0.1%
305 112
 
< 0.1%
330 111
 
< 0.1%
Other values (51325) 233985
99.5%
ValueCountFrequency (%)
8.5 1
 
< 0.1%
8.95 1
 
< 0.1%
9.1 1
 
< 0.1%
9.15 1
 
< 0.1%
9.3 1
 
< 0.1%
9.5 1
 
< 0.1%
9.55 3
< 0.1%
9.6 3
< 0.1%
9.65 3
< 0.1%
9.7 1
 
< 0.1%
ValueCountFrequency (%)
32468.1 1
< 0.1%
32400 1
< 0.1%
32301 1
< 0.1%
32300.05 1
< 0.1%
32300 1
< 0.1%
32290 1
< 0.1%
32200 1
< 0.1%
32136.85 1
< 0.1%
32082.25 1
< 0.1%
31850 1
< 0.1%

Close
Real number (ℝ)

HIGH CORRELATION 

Distinct63739
Distinct (%)27.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1266.5544
Minimum9.15
Maximum32861.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-05-24T23:17:11.957307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.15
5-th percentile97.25
Q1274.35
median566.7
Q31242.4
95-th percentile3977.89
Maximum32861.95
Range32852.8
Interquartile range (IQR)968.05

Descriptive statistics

Standard deviation2582.1409
Coefficient of variation (CV)2.0387131
Kurtosis48.842313
Mean1266.5544
Median Absolute Deviation (MAD)368.1
Skewness6.2764931
Sum2.9788345 × 108
Variance6667451.8
MonotonicityNot monotonic
2024-05-24T23:17:12.139319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
137 32
 
< 0.1%
140 30
 
< 0.1%
200.15 30
 
< 0.1%
30 30
 
< 0.1%
270.05 29
 
< 0.1%
135 29
 
< 0.1%
157.1 29
 
< 0.1%
171.95 28
 
< 0.1%
284.8 28
 
< 0.1%
125.2 28
 
< 0.1%
Other values (63729) 234899
99.9%
ValueCountFrequency (%)
9.15 1
 
< 0.1%
9.4 1
 
< 0.1%
9.5 1
 
< 0.1%
9.6 1
 
< 0.1%
9.65 6
< 0.1%
9.75 3
< 0.1%
9.8 2
 
< 0.1%
9.85 2
 
< 0.1%
9.95 2
 
< 0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
32861.95 1
< 0.1%
32766.1 1
< 0.1%
32661.85 1
< 0.1%
32617.75 1
< 0.1%
32498.6 1
< 0.1%
32443.5 1
< 0.1%
32403.95 1
< 0.1%
32371.05 1
< 0.1%
32233.4 1
< 0.1%
32226.4 1
< 0.1%

Volume
Real number (ℝ)

Distinct220434
Distinct (%)93.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3045903.3
Minimum3
Maximum4.8105893 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-05-24T23:17:12.320746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile10423.55
Q1219009.5
median1010938.5
Q33019851
95-th percentile12098458
Maximum4.8105893 × 108
Range4.8105892 × 108
Interquartile range (IQR)2800841.5

Descriptive statistics

Standard deviation7333980.8
Coefficient of variation (CV)2.407818
Kurtosis372.03119
Mean3045903.3
Median Absolute Deviation (MAD)939437.5
Skewness12.461644
Sum7.1637209 × 1011
Variance5.3787274 × 1013
MonotonicityNot monotonic
2024-05-24T23:17:12.508209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 31
 
< 0.1%
200 24
 
< 0.1%
1000 21
 
< 0.1%
500 18
 
< 0.1%
300 17
 
< 0.1%
700 17
 
< 0.1%
900 16
 
< 0.1%
800 14
 
< 0.1%
400 14
 
< 0.1%
1400 14
 
< 0.1%
Other values (220424) 235006
99.9%
ValueCountFrequency (%)
3 1
 
< 0.1%
6 1
 
< 0.1%
10 1
 
< 0.1%
25 2
< 0.1%
27 2
< 0.1%
32 1
 
< 0.1%
33 1
 
< 0.1%
35 1
 
< 0.1%
50 4
< 0.1%
51 2
< 0.1%
ValueCountFrequency (%)
481058927 1
< 0.1%
479716245 1
< 0.1%
390577839 1
< 0.1%
316008609 1
< 0.1%
286857658 1
< 0.1%
286173629 1
< 0.1%
283614463 1
< 0.1%
270968028 1
< 0.1%
265709391 1
< 0.1%
262677081 1
< 0.1%

Month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4606322
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-05-24T23:17:12.664073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4571337
Coefficient of variation (CV)0.53510765
Kurtosis-1.2076079
Mean6.4606322
Median Absolute Deviation (MAD)3
Skewness0.010278371
Sum1519489
Variance11.951773
MonotonicityNot monotonic
2024-05-24T23:17:12.801079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 20705
8.8%
7 20486
8.7%
3 20106
8.5%
6 19795
8.4%
12 19744
8.4%
5 19743
8.4%
8 19586
8.3%
2 19286
8.2%
9 19161
8.1%
10 19049
8.1%
Other values (2) 37531
16.0%
ValueCountFrequency (%)
1 20705
8.8%
2 19286
8.2%
3 20106
8.5%
4 18627
7.9%
5 19743
8.4%
6 19795
8.4%
7 20486
8.7%
8 19586
8.3%
9 19161
8.1%
10 19049
8.1%
ValueCountFrequency (%)
12 19744
8.4%
11 18904
8.0%
10 19049
8.1%
9 19161
8.1%
8 19586
8.3%
7 20486
8.7%
6 19795
8.4%
5 19743
8.4%
4 18627
7.9%
3 20106
8.5%

Year
Real number (ℝ)

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2010.8936
Minimum2000
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-05-24T23:17:12.947771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2001
Q12006
median2011
Q32016
95-th percentile2020
Maximum2021
Range21
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.9434747
Coefficient of variation (CV)0.0029556386
Kurtosis-1.105293
Mean2010.8936
Median Absolute Deviation (MAD)5
Skewness-0.12021592
Sum4.7294608 × 108
Variance35.324892
MonotonicityNot monotonic
2024-05-24T23:17:13.145984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2020 12348
 
5.3%
2012 12299
 
5.2%
2013 12250
 
5.2%
2015 12152
 
5.2%
2017 12152
 
5.2%
2010 12132
 
5.2%
2011 12103
 
5.1%
2016 12103
 
5.1%
2018 12054
 
5.1%
2019 12005
 
5.1%
Other values (12) 113594
48.3%
ValueCountFrequency (%)
2000 7704
3.3%
2001 7864
3.3%
2002 8749
3.7%
2003 9014
3.8%
2004 9737
4.1%
2005 10487
4.5%
2006 10587
4.5%
2007 10790
4.6%
2008 11368
4.8%
2009 11418
4.9%
ValueCountFrequency (%)
2021 3920
 
1.7%
2020 12348
5.3%
2019 12005
5.1%
2018 12054
5.1%
2017 12152
5.2%
2016 12103
5.1%
2015 12152
5.2%
2014 11956
5.1%
2013 12250
5.2%
2012 12299
5.2%

Interactions

2024-05-24T23:17:06.899204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:16:57.345869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:16:58.795323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:16:59.987127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:17:01.519151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:17:02.831483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:17:04.195993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:17:05.736362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:17:07.059410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:16:57.535635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:16:58.949344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:17:00.138413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:17:01.672123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:17:02.980989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:17:04.466028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:17:05.883852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:17:07.204108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:16:57.721631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:16:59.107126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:17:00.292557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:17:01.816839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:17:03.127151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:17:04.749435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:17:06.036931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:17:07.349481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:16:57.894936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:16:59.257785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:17:00.524336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:17:01.967662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:17:03.276190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:17:04.928986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:17:06.179090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:17:07.489440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:16:58.060473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:16:59.403349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:17:00.745541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:17:02.111870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:17:03.424932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:17:05.083147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:17:06.355110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:17:07.629265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:16:58.222220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:16:59.557688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:17:00.967185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:17:02.250052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:17:03.579962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:17:05.229126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:17:06.494339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:17:07.779413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:16:58.388092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:16:59.704803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:17:01.180540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:17:02.395515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:17:03.799358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:17:05.382419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:17:06.629244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:17:07.909173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:16:58.606643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:16:59.842985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:17:01.342361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:17:02.682258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:17:03.969333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:17:05.541087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-24T23:17:06.774031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-24T23:17:13.255835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
CloseHighLowMonthOpenPrev CloseVolumeYear
Close1.0001.0001.000-0.0021.0000.999-0.2510.287
High1.0001.0001.000-0.0031.0001.000-0.2520.284
Low1.0001.0001.000-0.0021.0001.000-0.2510.289
Month-0.002-0.003-0.0021.000-0.003-0.002-0.001-0.040
Open1.0001.0001.000-0.0031.0001.000-0.2520.286
Prev Close0.9991.0001.000-0.0021.0001.000-0.2520.287
Volume-0.251-0.252-0.251-0.001-0.252-0.2521.0000.463
Year0.2870.2840.289-0.0400.2860.2870.4631.000

Missing values

2024-05-24T23:17:08.124491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-24T23:17:08.494268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DateSymbolSeriesPrev CloseOpenHighLowCloseVolumeMonthYear
017-01-2012ADANIPORTSEQ135.50137.10141.00135.00140.00163619612012
118-01-2012ADANIPORTSEQ140.00142.00143.80138.70141.7089059112012
219-01-2012ADANIPORTSEQ141.70144.00150.55143.15149.40145607712012
320-01-2012ADANIPORTSEQ149.40151.90157.60150.25155.40163407012012
423-01-2012ADANIPORTSEQ155.40155.40155.40145.10146.75165760912012
524-01-2012ADANIPORTSEQ146.75147.05152.90145.60150.05133736212012
625-01-2012ADANIPORTSEQ150.05150.95150.95142.25143.20185961712012
727-01-2012ADANIPORTSEQ143.20145.80149.65144.80147.10126448312012
830-01-2012ADANIPORTSEQ147.10147.10147.40137.35138.4075769412012
931-01-2012ADANIPORTSEQ138.40138.95148.50137.00146.25129134412012
DateSymbolSeriesPrev CloseOpenHighLowCloseVolumeMonthYear
23518213-02-2007ZEETELEEQ258.35260.00276.95252.25267.95268995022007
23518314-02-2007ZEETELEEQ267.95272.00277.00256.50265.50282044422007
23518415-02-2007ZEETELEEQ265.50269.90273.50265.10268.25351674822007
23518519-02-2007ZEETELEEQ268.25269.25272.00258.25260.50138100822007
23518620-02-2007ZEETELEEQ260.50262.00262.85247.60249.75201747822007
23518721-02-2007ZEETELEEQ249.75254.45259.00249.00255.15158704222007
23518822-02-2007ZEETELEEQ255.15256.90259.70250.30254.10203521622007
23518923-02-2007ZEETELEEQ254.10255.80255.80244.50248.7597881622007
23519026-02-2007ZEETELEEQ248.75253.80253.80242.80244.3083371422007
23519127-02-2007ZEETELEEQ244.30246.00248.15231.00238.35274597922007